Satellite radar altimetry signals from glaciers and ice sheets represent a combination of scattering from ice-surface topography and the snow and firn volume. Changes in surface topography through time, even at sub-footprint scales, will change the interaction between the wavefront and surface, impacting the shape of the return waveform. Here, we describe a new facet-scattering model that uses arbitrary topographic inputs and can be used for testing hypotheses about ice-sheet surface change in the historical, three-decade-long pulse-limited radar altimetry record. We demonstrate the scattering model on both synthetic and real topographies, showing how off-nadir features and a deforming surface impact the returned waveform. We anticipate this simulator can be used to quantify sub-footprint surface change and the evolution of dynamic ice-sheet processes on rugged, high velocity ice streams and outlet glaciers in the decades before our modern, continuous laser altimetry and high-resolution stereophotogrammetric records capable of observing such changes began.

In this study, we present an unsupervised methodology for detecting moving targets using Capella Space’s latest generation SAR sensor. The sensor has the capability to dwell on a target for an extended period of time in its spot-light (SP) mode, which we take advantage of to track moving objects in an acquisition. An approach that combines a signal-processing-based workflow with an image-domain-based one is presented. From one side, the long-dwell SAR image is exploit by doing an interfeometric processing of different azimuth sub-apertures from the long-dwell. From another side, a kernelized cross correlation technique is used. By combining intermediate results from these complementary workflows, a smooth and robust track is obtained on the targets. The algorithm is demonstrated on a long-dwell spotlight obtained over a busy shipping channel with watercraft both small and large successfully tracked.

The ability to automatically detect hard targets is highly beneficial for an imagery analyst. Their initial task in the information gathering process is to identify and interpret these targets in a scene. By automating the detection process, the workflow can be expedited. This paper presents an algorithm for detecting hard targets in long dwell spotlight (SP) Capella Space SAR images. The spectrum of the SP image is divided into non-overlaping sub-bands in order to compute the interferometric coherence between them. The proposed algorithm is tested on real spaceborne Capella SAR data showing the potential to clearly identify hard targets such as buildings, vehicles, and other artificial man-made objects.

This paper presents an innovative approach to archaeological and geological exploration, combining Synthetic Aperture Radar (SAR) imagery, Ground Penetrating Radar (GPR), and advanced robotic algorithms. Utilizing SAR data captured by Capella, the study identifies areas of interest (AOIs) through supervised classification methods. These AOIs are then surveyed by a UAV equipped with GPR, optimized for efficient pathfinding and maximal coverage using robotic exploration algorithms. The survey generates high-resolution radar images, detailed digital elevation models, and orthomosaic images through photogrammetry, providing a comprehensive view of both surface and subsurface features.

Pits are depressions in the ground that occur due to the collapse of the surface layer. The characterization from orbit of their internal structure using optical images is challenging due to uncontrolled illumination geometry. In this paper, we propose a methodology for the characterization of pits’ interiors by exploiting Synthetic Aperture Radar (SAR) images. The methodology analyzes the amplitude and range of the radar echoes originating from the pit’s interior for determining its geometric characteristics through data inversion. The experimental results demonstrate that a set of bright reflections in the radar image can be attributed to the response of pits’ vertical walls and floor. By applying the proposed methodology and interpreting the radar reflections, we are able to derive a geometric characterization (e.g., depth) of a given pit. The retrieved geometric parameters from SAR data of a terrestrial pit denoted asWell of Barhoutalign well with the ground truth. The findings of this study have implications for both Earth observation and planetary exploration.

Circular sinkholes (i.e., pits) are depressions in the ground caused by the collapse of the surface layer. These type of features are commonly found on Earth and on other celestial bodies such as the Moon and Mars. Sinkholes may provide access to an underground cave system but it is very difficult to understand their accessibility and internal shape from optical images. In this paper, we propose a methodology for characterizing circular sinkholes by exploiting X-Band Very High Resolution (VHR) Spaceborne Synthetic Aperture Radar (SAR) images. The analysis of our experimental results show that a set of bright reflections in the radar image can be attributed to the response of the circular pit vertical walls and interior. Accordingly, it is possible to characterize the internal morphology of the pit. The results of this work have implications for planetary exploration as the proposed methodology can be applied for characterizing planetary pits.

Taking advantage of Capella’s ability to dwell on a target for an extended period of time (nominally 30s) in its spotlight (SP) mode, an unsupervised methodology for detecting moving targets in this data is presented in this paper. By colourizing short segments (sub-apertures) of the total imaging time, a colourised sub-aperture image (CSI) can be formed. This can be used in conjunction with well-established computer vision techniques to detect moving targets and track them in the SP image. In essence, the moving target detection problem is transformed from temporal image stack identification to colour segmentation in a single image. The presented detection and tracking are wholly unsupervised. Additionally, computer-vision-based tracking algorithms are demonstrated on detected movers and qualitatively assessed for accuracy of tracking.

Capella Space is the first US commercial company to build, launch, and operate a constellation of synthetic aperture radar satellites capable of collecting very high resolution SAR imagery. All satellites in the constellation carry an X-band radar capable of acquiring imagery in spotlight, sliding spotlight, and stripmap modes. In 2023, Capella will launch the first of a new generation of satellites names Acadia. These satellites will provide high quality imagery and lay the platform for advanced SAR data products, such as interferometric SAR and bistatic imagery.

Caves are one of the last frontiers of human exploration on Earth. They are very relevant scientific targets as they host significant biodiversity and unique geologic formations. The presence of underground passages accessible for human or robotic exploration are revealed by localized collapse of the near-surface ceiling of a cave system (skylight). Remote sensing systems are a valuable tool for skylights detection as these features are often located on very remote and often inaccessible regions of the Earth. However, with the available remote sensing techniques and data analysis methodologies, it is very difficult to determine whether a skylight is providing access to a cave continuation or it represents only a closed depression with no extensions. In this article we propose a methodology, based on very high-resolution (VHR) orbital synthetic aperture radar (SAR) imaging systems, to estimate both caves geometric characteristics and accessibility information in the proximity of a skylight. To test our methodology, we acquired radar data over different Earth’s location by exploiting the Capella Space X-band microsatellite radar constellation. The experimental results show that our methodology effectively determines the caves geometric characteristics and accessibility under a variety of surface conditions. We also detected several unknown and unexplored large cave systems located near Volcan Wolf and Ecuador, Isla Isabela, Galapagos. The presented work has relevant implications for the field of geological studies, ecology, and space exploration research since optical imaging shows the evidence of potential cave systems accessible from skylights on other planetary bodies such as Mars.

Cave systems are one of the last frontiers of human exploration on both Earth and other celestial bodies. In this context, lava tubes are natural subsurface tunnels, which are not visible from the surface, that are ubiquitous on Earth as well as on the Moon and Mars. Skylights are one of the surface evidences of the presence of such conduits in the form of overhanging collapses of the cave ceiling, making these cavities partially observable and potentially accessible. In this paper, we propose a method for imaging and characterizing subsurface structures by spaceborne VHR SAR imaging. We performed several acquisitions over different Earth locations by exploiting Capella's X-band VHR SAR imaging radars in spotlight mode. The obtained results show that the proposed methodology is able to characterize the main geometric parameters of the first section of a lava tube (e.g. width, height) in the surroundings of a skylight and it provides an indication of the actual subsurface accessibility. The proposed methodology has several important implications for exploration and could be applied to different type of cavities other than lava tubes.

Capella's first commercial Synthetic Aperture Radar (SAR) satellite was launched in August 2020. After more than one year of successful operations, Capella plans a continuous growth of both number of satellites and satellite capabilities. New tasking patterns allow the collection of pairs and time series of very high resolution (VHR) SAR images. A novel repeat tasking request pattern will enable SAR applications such as change detection and the exploitation of interferometric SAR (InSAR) techniques. The combination of on-board GPUs and dedicated on-board processing algorithms will be used for rapid target detection and minimized satellite downlink. On-board processing, applicable in many quasi real-time operational scenarios, can also be used for tipping and cueing other satellites to immediately capture a high resolution imagery. Finally, Capella Space Open Data program started in 2021 with new images added each quarter.

The rapid delineation of water extent in a flood-type event can be very beneficial to disaster relief efforts, and Synthetic Aperture Radar (SAR) is a modality ideally suited for such mapping. However, in a rapid-response scenario, it is desirable to produce such maps independent of historical or external data. To this end, we have propose a scheme to produce flood event maps from a single high-resolution StripMap (SM) imagery acquired from the Capella Space X-band VHR SAR constellation. The learning algorithm is solely trained on publicly available ancillary data, without the use of any human generated labels. The flood-maps are validated quantitatively on non-event scenes against water-occurrence data and qualitatively over the course of a flood-event caused by Hurricane Ida's landfall.

Vegetation water stress is a key control on wildfire risk, tree mortality, and ecosystem water and carbon fluxes. Although active microwave remote sensing methods have been used to estimate vegetation water, they remain poorly validated because of the immense mismatch between the scale of radar pixel resolutions (100 m to 25 km) and field measurements (individual trees). In this study, we present a new plot-scale vegetation water measurement technique using a side-facing bistatic radar. Using field experiments and a matched filtering technique to isolate the radar signal from noise, we show that radar amplitude is sensitive to xylem water potential (a measure of tree water status). However, our results are affected by periodic noise (period of~12 hours), which may be due to radio frequency interference. We discuss potential pathways to isolate the signal and the implications of the new tree water status measurement system for global validation of microwave remote sensing.

Capella Space launched its first commercial Synthetic Aperture Radar (SAR) satellite in August 2020. After commissioning phase, Capella started commercial operations in January 2021, deploying a fully-functional SAR system that demonstrates three engineering milestones: an 8-m2deployable reflector mounted on small and agile satellite; the capability to process and timely deliver 0.5 m resolution spotlight images with 9 looks; and an automated tasking and delivery system that simplifies the ordering of high-quality SAR imagery for expert and novice users. In this paper, we present results from the commissioning and calibration/validation operations. We also compare images collected with the variety of imaging modes that Capella systems can collect.

Owing to the unique all-weather, day-night imaging capabilities of Synthetic Aperture Radar (SAR) imaging, the modality is advantageous in the detection of anthropogenic activity. In this paper we present a fully unsupervised change detection framework that operates on Very High Resolution (VHR) SAR image pairs to produce a binary change map, without a need for per-image parameter setting. The framework is demonstrated on a pair of Capella-2 VHR X-band imagery acquired over San Diego, USA.

Many applications utilizing SAR data, such as change detection, segmentation and classification, are impaired by the multiplicative speckle interference inherent in the imagery. Thus despeckling of SAR imagery is a often the key to developing robust algorithms for scene understanding. In recent years numerous deep learning-based approaches to speckle reduction have been proposed. However, the performance of these methods has largely failed to meet the expectations of researchers and industry alike. A key reason for this is due to the lack of accurate SAR-based ground truth training data. In this paper we propose the use of very high-resolution (VHR), low speckle aerial imagery and an accurate speckle model, as a ground truth signal for training a despeckling network based on the DnCNN architecture. Furthermore, we investigate modifications to the training formulation and finally demostrate the approach on Capella-2 VHR X-band imagery.

Englacial layer velocity can provide insights on the vertical-velocity structure of the ice sheets. We present a repeat-pass interferometric approach that allows the estimation of the vertical englacial layer velocity using the radar sounder data. In contrast to the ground-based sensors, the airborne radar sounder data can potentially be used to estimate the layer velocity on a continental scale. When merged with the horizontal surface velocity and the numerical models, layer velocity can support the 3-D analysis of the ice flow and structure. Our aim is to provide the proof-of-concept demonstration that, similar to the side-looking synthetic aperture radar for imaging, the airborne radar sounder data can be used to estimate the subwavelength displacement of the englacial radio-stratigraphic layers. To achieve this, we use the phase and magnitude acquired repeatedly over the same region of interest. After the extraction of the crossing points, two acquisitions are finely registered. Then, we compute the interferometric phase for the englacial layers (which are shown to behave as PSs in the nadir sounding geometry), to estimate a vertical displacement and a velocity profile. We present the results over East Antarctica using data from the high-capability radar sounder (HiCARS) system. We show two scenarios that demonstrate the feasibility, limitations, and requirements of this approach.

Recent work has demonstrated a passive radio sounding approach that uses the Sun as a source for echo detection and ranging. As the Sun is a moving source with a position that is knowna priori, we evaluate this technique’s capabilities to measure the echo’s phase history, map topography, and perform synthetic aperture radar (SAR) focusing. Here, we present our approach to implementing passive SAR using a compact, temporally incoherent radio-astronomical source as a signal of opportunity. We first evaluate the passive system’s capabilities to obtain an echo from a rough surface by determining the critical signal-to-noise ratio (SNR) for reliably observing the Sun’s echo reflection with our passive instrument. We then demonstrate that our technique can detect the necessary changes in range, phase, and reflectivity of an echo from the Sun. We next present the experimental results of our passive radar testing using the Sun at Dante’s View, Death Valley, to highlight this technique’s ability to perform 2-D imaging. Finally, with synthetic data, we demonstrate that we can use time-domain backprojection to focus a planar white noise signal, perform passive SAR imaging, and improve the measurement’s SNR and azimuth resolution. The results of passive SAR focusing on white noise highlight the potential for the Sun and Jupiter’s radio emissions to perform surface and subsurface imaging for planetary and terrestrial observations.

We summarize the key elements of the operational readiness of Sequoia, the first commercially-operational satellite SAR system built and launched by Capella Space. We provide an overview the radar system, and the space operations and commissioning plan. Sample imagery collected with Capella radar hardware on an airborne platform is used to demonstrate the imaging capability of Sequoia.

Recent work has demonstrated a passive radio sounding approach using the Sun as a source for echo detection. We expand on our passive autocorrelation-based technique by demonstrating its potential to map topography as the Sun moves throughout the entire day. Here, we show with synthetic data and our experimental results of passive radar testing with the Sun at Dante's View, Death Valley, our approach to implementing two dimensional image formation with passive radar. We also determine the critical signal to noise ratio (SNR) required to reliably observe a Sun echo with our passive instrument. Finally, we show that our autocorrelation-based technique can obtain changes in range, reflectivity, and phase, which are measurements normally acquired with traditional active radar systems. Demonstrating that our technique can acquire these changes is the first step to developing passive SAR processing using the Sun.

Ice penetrating radar sounders are the primary geophysical instruments for profiling the subsurface of terrestrial [1] and planetary [2]-[6] cryospheres. The capability of radar sounders to detect subsurface interfaces beneath thick and/or lossy ice is a function of their link budget [6]-[7] which must be much greater than surface imaging or altimetric radars [8] to achieve deep penetration. We propose a coherent along-feature processing approach for radar sounding data that maximizes the achievable azimuth gain for applications, like the exploration of icy moons [6]-[7] or mapping of deep/warm outlet glacier beds [9], where detecting very weak reflections from continuous interfaces is critical.

The Capella constellation of 36 X-band SAR Satellites will provide global hourly imaging opportunities for the entire earth and InSAR collection intervals of 4 hours. With the successful launch of the first satellite last year, Capella is prepared to launch the first operational this year and the first incrementally launch 36 satelites over two years. In addition to providing short revisit times, Capella will provide game-changing image request and delivery services where users can request imagery on-demand and receive it in a matter of minutes not days or weeks.

Ice crystal orientation fabric (COF) records information about past ice-sheet deformation and influences the present-day flow of ice. Polarimetric radar sounding provides a means to infer anisotropic COF patterns due to the associated birefringence of polar ice. Here, we develop a polarimetric coherence (phase-based) method to determine horizontal properties of the COF. The method utilizes the azimuth and depth dependence of the vertical gradient of the hhvv coherence phase to infer the dielectric principal axes and birefringence, which are then related to the second-order fabric orientation tensor. Specifically, under the assumption that one of the orientational eigenvectors is vertical, we can determine the horizontal eigenvectors and the difference between the horizontal eigenvalues (a measure of horizontal fabric asymmetry). The method exploits single-polarized data acquired with varying antenna orientation. It applies to ground-based “multi-polarization” surveys and is demonstrated using data acquired by Center for Remote Sensing of Ice Sheets (CReSIS) using Multi-Channel Coherent Radar Depth Sounder (MCRDS) from the North Greenland Eemian Ice Drilling (NEEM) ice core region in Greenland. The analysis is validated using a combination of polarimetric matrix backscatter simulations and comparison with COF data from the NEEM ice core. The results are consistent with a conventional model of ice deformation at an ice divide where a lateral tension component is present, with minor horizontal COF asymmetry and the greatest horizontal concentration of crystallographic axes orientated near parallel to the ice divide.

Radar sounders represent a powerful remote sensing solution to probe icy surface and detect subsurface layers by tracking vertical dielectric discontinuities. These englacial layers are fundamental indicator of the dynamic, rheological, and subglacial configuration of an ice sheet. However, steep layers can disappear when along-track processing is applied to radar sounder data. In this work, we present a novel Synthetic Aperture Radar (SAR) processing technique that aims to enhance the post processing Signal to Noise Ratio (SNR) of subsurface layers and avoiding interference in data processing. The proposed technique uses the phase and magnitude of a single channel RS system in a four step approach, made up of: i) sidelobes weighting, ii) range compression, iii) phase-shifted along-track coherent summation, and iv) multilooking. Additionally, the proposed technique allows the automatic estimation of the slope of layers. The proposed processing method has been validated on airborne radar sounder data acquired in Antarctica, while the slope estimation result is theoretically demonstrated.

Reliable electromagnetic simulators are of prime importance for the design of radar sounder instruments and for supporting the subsequent interpretation of their data. In this paper we present a coherent simulator based on the facet method that can compute radar echoes from the subsurface of a target area with an arbitrary number of geological layers, thus going beyond the mere 1- or 2-layer descriptions usually modelled by coherent ray-tracing radar sounder simulators. The simulator has been validated using real radar data of lunar areas characterized by a multilayer nature collected by the Lunar Radar Sounder (LRS) instrument of JAXA's Kaguya probe. Results confirm the effectiveness of the proposed simulator.

While radio echo sounders are powerful tools used to constrain subglacial conditions, current ice-penetrating radar systems are too resource intensive for multiyear deployment at a large scale. To address this, we present passive radio sounding as a low resource approach for observing the subsurface of ice sheets and glaciers. Although passive radar has been used for target tracking and military purposes, it has never been implemented for ice sounding. Nevertheless, recent work has proposed the passive radio sounding of Europa's icy shell using Jupiter's decametric radiation as a source for echo detection [1], [2]. Expanding on this idea, we evaluate and discuss the challenges of developing a passive radio sounder that uses the Sun for echo detection. Our prototype measures the Sun's direct and reflected path off the ocean to obtain the height of a cliff along the California coast. This serves as the first in-situ demonstration of an autocorrelation-based passive-sounder using a compact astronomical white noise source.

Reliable electromagnetic simulators are of prime importance for the design of radar sounder instruments and for supporting the subsequent analysis of their data. In this paper, we present a coherent, facet method-based simulator that can compute radar echoes from the subsurface of a target area with an arbitrary number of geological layers, thus going beyond the surface-only or the two-layer descriptions so far implemented in coherent ray-tracing radar sounder simulators. Propagation of fields throughout the subsurface is computed according to Snell's law following a ray-tracing approach. For each ray interacting with the surface, be it a direct reflection or a refracted ray coming from the subsurface, the phase contribution of each facet is calculated through the linear phase approximation, while the total field received at the antenna is computed using Huygen's principle. Validation simulations have been performed against the radar data of lunar and martian areas characterized by a multilayer nature and collected by the Lunar Radar Sounder instrument of JAXA's Kaguya lunar probe and the Shallow Radar instrument onboard NASA's Mars Reconnaissance Orbiter, respectively. Results confirm the effectiveness of the proposed simulator.

Ice sheet contributions to sea level rise present one of the greatest challenges that our society will face in the next century. However, models predicting sea level rise due to ice melt lack critical information regarding processes at the base of ice sheets. Although radio echo sounders are powerful tools that are currently used to constrain subglacial conditions, existing ice-penetrating radar systems are too resource-intensive in terms of cost, power, and logistics for multiyear deployment at a large scale. To address this, we present passive radio sounding as a low-resource approach for observing ice sheets across a range of spatial and temporal scales. While passive radar has been used for target tracking and military purposes, it has never been used for the sounding of ice sheets. Some recent work has proposed using passive radio sounding of Europa's icy shell using Jupiter's decametric radiation. We expand on this idea by evaluating and discussing challenges and opportunities for developing a passive radio sounder using the Sun as an illuminator of opportunity for echo detection. Here, our prototype instrument sits on the side of a cliff and measures the Sun's direct and reflected path off the ocean surface. We then use an autocorrelation-based method to extract the amplitude and delay of the reflection. This serves as the first in situ demonstration of an autocorrelation-based passive sounder using a compact astronomical white noise signal.

Surface cross-track clutter can corrupt both earth and planetary radar sounder (RS) observations preventing definitive interpretation of subsurface features, which are often of primary interest to geologists and planetary scientists. This clutter is usually identified either by manual or automatic techniques that require ancillary information about the topography of the surface, or by using multichannel RS systems with arrays of antennas. However, topographic information is not always available and multichannel systems are generally too massive and costly to mount on satellites for the planetary exploration. In this paper, we propose a novel approach to clutter discrimination that is independent of ancillary information and limits the hardware complexity of the RS system. This approach uses a two-channel RS and exploits cross-channel interferometric phase differences to discriminate the clutter. Our approach includes three main steps: 1) manual feature extraction and theoretical phase-difference estimation; 2) RS interferogram formation; and 3) comparison of theoretical and real phase difference distributions. The proposed method was validated on RS data acquired in Greenland and provides a proof of concept for the surface clutter discrimination using RS data.

This paper presents a novel hybrid method to the estimation of bio/geophysical parameters, which models and corrects deviations from correct target values when theoretical electromagnetic models are used for the inversion process. The proposed hybrid method integrates theoretical models with empirical observations associated to a few field reference samples. This is achieved based on two steps. In the first step, deviations between estimations obtained by a theoretical model and empirical observations are initially computed. Then, deviations associated to unlabeled samples (for which reference measures are not existing) are characterized based on two different strategies: 1) the global deviation bias strategy (which assumes that the deviations of samples are constant within the input space); and 2) the local deviation bias strategy (which assumes that the deviations of samples are variable within different portions of the input space). In the second step, the theoretical model estimates of unlabeled samples are corrected based on the estimated deviations. The experimental analysis carried out in the context of soil moisture content retrieval from microwave remotely sensed data confirms the effectiveness of the proposed hybrid estimation method.

This paper presents the Radar for Icy Moon Exploration (RIME) that is a fundamental payload in the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (ESA). RIME is a radar sounder aimed to study the subsurface of Jupiter's icy moons Ganymede, Europa and Callisto. The paper illustrates the main goals of RIME, its architecture and parameters and some recent advances in its design.

Surface clutter can corrupt both Earth and planetary Radar Sounder (RS) observations preventing definitive interpretation of subsurface features, which are often the primary interest of geologists and planetary scientists. Clutter is usually detected by manual or automatic techniques that require ancillary information about the topography of the surface. However, this topography information is not always available. In this paper, we propose a novel method for clutter detection that is independent from ancillary information. This method uses a two channel RS system to exploit the cross-channel interferometric phase difference and is made up three main steps: i) feature extraction and theoretical phase difference estimation, ii) RS interferogram formation and iii) comparison of theoretical and real phase difference distributions. The proposed method has been validated on RS data acquired in Greenland.

In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve for the two datasets shows a slight underestimation of SMC in the ASAR product. This could be ascribed to the reduced resolution of ASAR WS and the use of VV polarization.